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]]>A “controller” refers to an entity that “determines the purposes and means” of how personal information will be processed.[1] Determining the “means” of processing refers to deciding “how” information will be processed.[2] That does not necessitate, however, that a controller makes every decision with respect to information processing. The European Data Protection Board (EDPB) distinguishes between “essential means” and “non-essential means.[3] “Essential means” refers to those processing decisions that are closely linked to the purpose and the scope of processing and, therefore, are considered “traditionally and inherently reserved to the controller.”[4] “Non-essential means” refers to more practical aspects of implementing a processing activity that may be left to third parties – such as processors.[5]
A “processor” refers to a company (or a person such as an independent contractor) that “processes personal data on behalf of [a] controller.”[6]
Data typically is needed to train and fine-tune modern artificial intelligence models. They use data – including personal information – in order to recognize patterns and predict results.
Whether an organization that utilizes personal information to train an artificial intelligence engine is a controller or a processor depends on the degree to which the organization determines the purpose for which the data will be used and the essential means of processing. The following chart discusses these variables in the context of training AI:
The following chart discusses these variables in the context of training AI:
Function |
Activities Indicative of a Controller |
Activities Indicative of a Processor |
Purpose of processing |
||
|
Why the AI is being trained. |
If an organization makes its own decision to utilize personal information to train an AI, then the organization will likely be considered a “controller.” |
If an organization is using personal information provided by a third party to train an AI, and is doing so at the direction of the third party, then the organization may be considered a processor. |
Essential means |
||
|
Data types used in training. |
If an organization selects which data fields will be used to train an AI, the organization will likely be considered a “controller.” |
If an organization is instructed by a third party to utilize particular data types to train an AI, the organization may be a processor. |
|
Duration personal information is held within the training engine |
If an organization determines how long the AI can retain training data, it will likely be considered a “controller.” |
If an organization is instructed by a third party to use data to train an AI, and does not control how long the AI may access the training data, the organization may be a processor. |
|
Recipients of the personal information |
If an organization determines which third parties may access the training data that is provided to the AI, that organization will likely be considered a “controller.” |
If an organization is instructed by a third party to use data to train an AI, but does not control who will be able to access the AI (and the training data to which the AI has access), the organization may be a processor. |
|
Individuals whose information is included |
If an organization is selecting whose personal information will be used as part of training an AI, the organization will likely be considered a “controller.” |
If an organization is being instructed by a third party to utilize particular individuals’ data to train an AI, the organization may be a processor. |
[1] GDPR, Article 4(7).
[1] GDPR, Article 4(7).
[2] EDPB, Guidelines 07/2020 on the concepts of controller and processor in the GDPR, Version 1, adopted 2 Sept. 2020, at ¶ 33.
[3] EDPB, Guidelines 07/2020 on the concepts of controller and processor in the GDPR, Version 1, adopted 2 Sept. 2020, at ¶ 38.
[4] EDPB, Guidelines 07/2020 on the concepts of controller and processor in the GDPR, Version 1, adopted 2 Sept. 2020, at ¶ 38.
[5] EDPB, Guidelines 07/2020 on the concepts of controller and processor in the GDPR, Version 1, adopted 2 Sept. 2020, at ¶ 38.
[6] GDPR, Article 4(8).
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]]>The post EDPB on Dark Patterns: Lessons for Marketing Teams appeared first on The National Law Forum.
]]>“Dark patterns” are becoming the target of EU data protection authorities, and the new guidelines of the European Data Protection Board (EDPB) on “dark patterns in social media platform interfaces” confirm their focus on such practices. While they are built around examples from social media platforms (real or fictitious), these guidelines contain lessons for all websites and applications. The bad news for marketers: the EDPB doesn’t like it when dry legal texts and interfaces are made catchier or more enticing.
To illustrate, in a section of the guidelines regarding the selection of an account profile photo, the EDPB considers the example of a “help/information” prompt saying “No need to go to the hairdresser’s first. Just pick a photo that says ‘this is me.’” According to the EDPB, such a practice “can impact the final decision made by users who initially decided not to share a picture for their account” and thus makes consent invalid under the General Data Protection Regulation (GDPR). Similarly, the EDPB criticises an extreme example of a cookie banner with a humourous link to a bakery cookies recipe that incidentally says, “we also use cookies”, stating that “users might think they just dismiss a funny message about cookies as a baked snack and not consider the technical meaning of the term “cookies.”” The EDPB even suggests that the data minimisation principle, and not security concerns, should ultimately guide an organisation’s choice of which two-factor authentication method to use.
Do these new guidelines reflect privacy paranoia or common sense? The answer should lie somewhere in between, but the whole document (64 pages long) in our view suggests an overly strict approach, one that we hope will move closer to commonsense as a result of a newly started public consultation process.
Let us take a closer look at what useful lessons – or warnings – can be drawn from these new guidelines.
According to the EDPB, dark patterns are “interfaces and user experiences […] that lead users into making unintended, unwilling and potentially harmful decisions regarding the processing of their personal data” (p. 2). They “aim to influence users’ behaviour and can hinder their ability to effectively protect their personal data and make conscious choices.” The risk associated with dark patterns is higher for websites or applications meant for children, as “dark patterns raise additional concerns regarding potential impact on children” (p. 8).
While the EDPB takes a strongly negative view of dark patterns in general, it recognises that dark patterns do not automatically lead to an infringement of the GDPR. The EDPB acknowledges that “[d]ata protection authorities are responsible for sanctioning the use of dark patterns if these breach GDPR requirements” (emphasis ours; p. 2). Nevertheless, the EDPB guidance strongly links the concept of dark patterns with the data protection by design and by default principles of Art. 25 GDPR, suggesting that disregard for those principles could lead to a presumption that the language or a practice in fact creates a “dark pattern” (p. 11).
The EDPB refers here to its Guidelines 4/2019 on Article 25 Data Protection by Design and by Default and in particular to the following key principles:
One of the EDPB’s positions, while grounded in the principle of data minimisation, undercuts a security practice that has grown significantly over the past few years. In effect, the EDPB seems to question the validity under the GDPR of requests for phone numbers for two-factor authentication where e-mail tokens would theoretically be possible:
“30. To observe the principle of data minimisation, [organisations] are required not to ask for additional data such as the phone number, when the data users already provided during the sign- up process are sufficient. For example, to ensure account security, enhanced authentication is possible without the phone number by simply sending a code to users’ email accounts or by several other means.
31. Social network providers should therefore rely on means for security that are easier for users to re[1]initiate. For example, the [organisation] can send users an authentication number via an additional communication channel, such as a security app, which users previously installed on their mobile phone, but without requiring the users’ mobile phone number. User authentication via email addresses is also less intrusive than via phone number because users could simply create a new email address specifically for the sign-up process and utilise that email address mainly in connection with the Social Network. A phone number, however, is not that easily interchangeable, given that it is highly unlikely that users would buy a new SIM card or conclude a new phone contract only for the reason of authentication.” (emphasis ours; p. 15)
The EDPB also appears to be highly critical of phone-based verification in the context of registration “because the email address constitutes the regular contact point with users during the registration process” (p. 15).
This position is unfortunate, as it suggests that data minimisation may preclude controllers from even assessing which method of two-factor authentication – in this case, e-mail versus SMS one-time passwords – better suits its requirements, taking into consideration the different security benefits and drawbacks of the two methods. The EDPB’s reasoning could even be used to exclude any form of stronger two-factor authentication, as additional forms inevitably require separate processing (e.g., phone number or third-party account linking for some app-based authentication methods).
For these reasons, organisations should view this aspect of the new EDPB guidelines with a healthy dose of skepticism. It likewise will be important for interested stakeholders to participate in the consultation to explain the security benefits of using phone numbers to keep the “two” in two-factor authentication.
Recent decisions by EU regulators (notably two decisions by the French authority, the CNIL have led to speculation about whether EU rules effectively require website operators to make it possible for data subjects to withdraw consent to all cookies with one single click, just as most websites make it possible to give consent through a single click. The authorities themselves have not stated that this is unequivocally required, although privacy activists notably filed complaints against hundreds of websites, many of them for not including a “reject all” button on their cookie banner.
The EDPB now appears to side with the privacy activists in this respect, stating that “consent cannot be considered valid under the GDPR when consent is obtained through only one mouse-click, swipe or keystroke, but the withdrawal takes more steps, is more difficult to achieve or takes more time” (p. 14).
Operationally, however, it seems impossible to comply with a “one-click withdrawal” standard in absolute terms. Just pulling up settings after registration or after the first visit to a website will always require an extra click, purely to open those settings. We expect this issue to be examined by the courts eventually.
The EDPB’s guidelines contain several examples of wording that is intended to convince the user to take a specific action.
The photo example mentioned in the introduction above is an illustration, but other (likely fictitious) examples include the following:
The EDPB criticises the language used, stating that it is “emotional steering”:
“[S]uch techniques do not cultivate users’ free will to provide their data, since the prescriptive language used can make users feel obliged to provide a self-description because they have already put time into the registration and wish to complete it. When users are in the process of registering to an account, they are less likely to take time to consider the description they give or even if they would like to give one at all. This is particularly the case when the language used delivers a sense of urgency or sounds like an imperative. If users feel this obligation, even when in reality providing the data is not mandatory, this can have an impact on their “free will”” (pp. 17-18).
Similarly, in a section about account deletion and deactivation, the EDPB criticises interfaces that highlight “only the negative, discouraging consequences of deleting their accounts,” e.g., “you’ll lose everything forever,” or “you won’t be able to reactivate your account” (p. 55). The EDPB even criticises interfaces that preselect deactivation or pause options over delete options, considering that “[t]he default selection of the pause option is likely to nudge users to select it instead of deleting their account as initially intended. Therefore, the practice described in this example can be considered as a breach of Article 12 (2) GDPR since it does not, in this case, facilitate the exercise of the right to erasure, and even tries to nudge users away from exercising it” (p. 56). This, combined with the EDPB’s aversion to confirmation requests (see section 5 below), suggests that the EDPB is ignoring the risk that a data subject might opt for deletion without fully recognizing the consequences, i.e., loss of access to the deleted data.
The EDPB’s approach suggests that any effort to woo users into giving more data or leaving data with the organisation will be viewed as harmful by data protection authorities. Yet data protection rules are there to prevent abuse and protect data subjects, not to render all marketing techniques illegal.
In this context, the guidelines should in our opinion be viewed as an invitation to re-examine marketing techniques to ensure that they are not too pushy – in the sense that users would in effect truly be pushed into a decision regarding personal data that they would not otherwise have made. Marketing techniques are not per se unlawful under the GDPR but may run afoul of GDPR requirements in situations where data subjects are misled or robbed of their choice.
Finally, the EDPB highlights some other “best practices” throughout its guidelines. We have combined them below for easier review:
These guidelines (available online) are subject to public consultation until 2 May 2022, so it is possible they will be modified as a result of the consultation and, we hope, improved to reflect a more pragmatic view of data protection that balances data subjects’ rights, security, and operational business needs. If you wish to contribute to the public consultation, note that the EDPB publishes feedback it receives (as a result, we have occasionally submitted feedback on behalf of clients wishing to remain anonymous).
Irrespective of the outcome of the public consultation, the guidelines are guaranteed to have an influence on the approach of EU data protection authorities in their investigations. From this perspective, it is better to be forewarned – and to have legal arguments at your disposal if you wish to adopt an approach that deviates from the EDPB’s position.
Moreover, these guidelines come at a time when the United States Federal Trade Commission (FTC) is also concerned with dark patterns. The FTC recently published an enforcement policy statement on the matter in October 2021. Dark patterns are also being discussed at the Organisation for Economic Cooperation and Development (OECD). International dialogue can be helpful if conversations about desired policy also consider practical solutions that can be implemented by businesses and reflect a desirable user experience for data subjects.
Organisations should consider evaluating their own techniques to encourage users to go one way or another and document the justification for their approach.
Article By Peter Craddock, Sheila A. Millar, and Tracy P. Marshall of Keller and Heckman LLP
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